Exploration
Jabar Habashi; Majid Mohammady Oskouei; Hadi Jamshid Moghadam
Abstract
The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission ...
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The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-spectral data was used to recognize argillic, sericite, propylitic, and iron oxide alterations associated with copper mineralization. For this purpose, two categories (porphyry copper-iron and advanced argillic-iron) related alterations were considered to perform the classification of a 2617 square kilometer area using a neural network classification algorithm. To evaluate the accuracy of the classifier, the confusion matrix was computed, which provides overall accuracy and the kappa coefficient factors for assessing classification accuracy. As a result, 64.17% and 83.5% of overall accuracy, and 0.602 and 0.807 of the kappa coefficient were achieved for the advanced argillic alterations and porphyry copper categories, respectively. Ultimately, the validation of the classifications was carried out using the normalized score (NS) equation, employing quantitative criteria. Notably, the advanced argillic class emerged with the top normalized score of 2.25 out of 4, signifying a 56% alignment with the geological characteristics of the region. Consequently, this outcome has led to the identification of favorable areas in the central and northeastern parts of the studied area.
M. Mohammady Oskouei; S. Babakan
Abstract
This work aims to extract the mineralogical constituents of the Lahroud Hyperion scene situated in the NW of Iran. Like the other push-broom sensors, Hyperion images suffer from spectral distortions, namely the smile effect. The corresponding spectral curvature is defined as an across-track wavelength ...
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This work aims to extract the mineralogical constituents of the Lahroud Hyperion scene situated in the NW of Iran. Like the other push-broom sensors, Hyperion images suffer from spectral distortions, namely the smile effect. The corresponding spectral curvature is defined as an across-track wavelength shift from the nominal central wavelength, and alters the pixel spectra. The common “column mean adjusted in MNF space” method was employed in this work to improve the processing accuracy by minimizing the smile effect before carrying out the atmospheric and topographical corrections. The mineral distributions were mapped by applying the standardized hyperspectral processing methodology developed by analytical imaging and geophysics (AIG). The spectral unmixing of the data resulted in the identification of five indicative minerals including natrolite, opal, analcime, kaolinite, and albite; and their spectra were employed for the generation of their distribution maps. Comparison of the results of the data processing with and without smile correction indicated a better classification performance after the smile correction. Quantitative validation of the final mineralogical map was performed using the 100 k geological map and reports of the region. Therefore, the coverage of the extracted minerals were investigated regarding the location of the lithological units in ArcGIS that implies a high coincidence. The mineral distributions in the final map show a high consistency with the geological map of the studied area, and thus it could be utilized successfully to reveal the mineralization trend in the region.
